Imputation methods for quantile estimation under missing at random

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Imputation is frequently used to handle missing data for which multiple imputation is a popular technique. We propose a fractional hot deck imputation which produces a valid variance estimator for quantiles. In the proposed method, the imputed values are chosen from the set of respondents and are assigned with proper fractional weights that use a density function for the working model. In addition, we consider a nonparametric fractional imputation method based on nonparametric kernel regression, avoiding a parametric distribution assumption and thus giving more robustness. The resulting estimator can be called nonparametric fractionally imputation estimator. Valid variance estimation is also discussed. A limited simulation study compares the proposed methods favorably with other existing methods
Publisher
INT PRESS BOSTON
Issue Date
2013
Language
English
Article Type
Article
Keywords

MULTIPLE-IMPUTATION; MEAN FUNCTIONALS

Citation

STATISTICS AND ITS INTERFACE, v.6, no.3, pp.369 - 377

ISSN
1938-7989
URI
http://hdl.handle.net/10203/213005
Appears in Collection
MA-Journal Papers(저널논문)
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